Condensed matter physics and materials science form a dynamic partnership, exploring how the collective behavior of atoms gives rise to the unique properties of solids and liquids. This field bridges the gap between fundamental quantum mechanics and the practical engineering of everything from flexible electronics to superconductors, turning abstract theories into tangible innovations that shape our daily lives.

At Gist.Science, we process every new preprint in this category directly from arXiv to make these complex discoveries accessible to everyone. Our team generates both plain-language overviews and detailed technical summaries for each paper, ensuring that researchers, students, and curious minds alike can grasp the latest breakthroughs without getting lost in dense jargon.

Below are the latest papers in condensed matter and materials science, organized by their most recent publication dates.

Competing crystallization pathways and cold crystallization kinetics in 10OS5 liquid crystal

This study investigates the competing crystallization pathways and cold crystallization kinetics of the liquid crystal 10OS5, revealing that its thermal history can be manipulated to tune the energy released during phase transitions, thereby highlighting its potential for thermal energy storage applications.

Aleksandra Deptuch, Mirosława D. Ossowska-Chruściel, Janusz Chruściel, Ewa Juszyńska-Gałązka2026-05-13🔬 cond-mat.mtrl-sci

Understanding oxide-thickness-dependent variability in dense Si-MOS quantum dot arrays

This study utilizes a 7x7 silicon quantum dot array fabricated via 300 mm CMOS and EUV lithography to demonstrate that a 17 nm gate oxide thickness optimizes uniformity by minimizing threshold voltage variability, thereby providing critical design guidelines for scalable quantum computing architectures.

Arne Loenders, Jacques Van Damme, Clement Godfrin, Paola Favia, Jacopo Franco, Thomas Van Caekenberghe, Bart Raes, Gulzat Jaliel, Sylvain Baudot, Luis Francisco Pinotti, Alexander Grill, George Simion (…)2026-05-13🔬 cond-mat.mtrl-sci

Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

This paper establishes a novel methodology combining X-ray photon correlation spectroscopy (XPCS) with domain-adaptive machine learning to quantitatively probe non-equilibrium grain boundary dynamics in nanocrystalline silicon, successfully extracting key kinetic parameters from complex experimental fluctuation maps that were previously inaccessible.

Mouyang Cheng, Bowen Yu, Chu-Liang Fu, Nina Andrejevic, Matthias T. Agne, Riley Hanus, Qiwei Wan, Nathan C. Drucker, Thanh Nguyen, Andrei Fluerasu, Lutz Wiegart, Xiaoqian M Chen, Daniel Pajerowski, Yo (…)2026-05-13🔬 cond-mat.mtrl-sci

Ordering governs magnetic tunability in FePt-based Janus particles independent of curvature

This study demonstrates that the magnetic tunability of micrometer-scale FePt-based Janus particles is governed primarily by chemical ordering rather than particle curvature, as evidenced by experiments and simulations showing that coercivity remains constant across varying diameters while being strongly dependent on L1_0 ordering.

Natalia Gonzalez-Vazquez, Eylül Suadiye, Eberhard Goering, Ruben O. Miranda-Rosales, Hilda David, Frank Thiele, Julia Unangst, Andrew K. Schulz, Gunther Richter2026-05-13🔬 cond-mat.mes-hall

Equivariant Space Group and Hamiltonian for Collinear Magnetic Systems

This paper introduces a symmetry-based framework using equivariant space groups to construct equivariant magnetic Hamiltonians (EMHs) that explicitly incorporate magnetic order parameters, enabling the study of magnetic-dynamics-driven topological phenomena and the accurate modeling of n-dependent band structures in both model and real materials.

Chaoxi Cui, Zhi-Ming Yu, Yilin Han, Run-Wu Zhang, Shengyuan A. Yang, Yugui Yao2026-05-13🔬 cond-mat.mtrl-sci

Automated multiphase identification and refinement in powder diffraction using mismatch-tolerant machine learning

This paper introduces RADAR-PD, a modality-aware machine learning framework that automates multiphase identification and refinement in both X-ray and neutron powder diffraction by combining mismatch-tolerant neural networks with physics-constrained verification to overcome existing bottlenecks in autonomous structural discovery.

Lalit Yadav, Yongqiang Cheng, Mathieu Doucet2026-05-13🔬 cond-mat.mtrl-sci

Anomalous Electrical Transport in the Kagome Magnet YbFe6_6Ge6_6

This study demonstrates that in the kagome magnet YbFe6_6Ge6_6, interactions between Fe and Yb moments induce a spin reorientation at low temperatures that closes the spin anisotropy gap and generates dynamic scalar spin chirality, resulting in an anomalous Hall effect despite the material's collinear antiferromagnetic order.

Weiliang Yao, Supeng Liu, Hodaka Kikuchi, Hajime Ishikawa, Øystein S. Fjellvåg, David W. Tam, Feng Ye, Douglas L. Abernathy, George D. A. Wood, Devashibhai Adroja, Chun-Ming Wu, Chien-Lung Huang, Bin (…)2026-05-12🔬 cond-mat.mtrl-sci

Short time-to-solution Quantum Monte Carlo for catalysed hydrogen synthesis. Tools give CO hydrolysis activation barriers to 1kJ/mol on Pt(111)

This paper demonstrates that a short time-to-solution Quantum Monte Carlo methodology, utilizing an embedded active site approach on a Pt(111) surface, accurately calculates CO hydrolysis activation barriers for hydrogen synthesis with a precision of approximately 1 kJ/mol, closely matching high-level configuration interaction benchmarks.

Ali Bagci, Philip E Hoggan2026-05-12🔬 cond-mat.mtrl-sci

Super Moiré Domain Tessellations, Sliding Ferroelectricity, and Reconfigurable Quantum Dot Arrays in Twisted Trilayer Hexagonal Boron Nitride

This paper demonstrates that twisted trilayer hexagonal boron nitride exhibits unique super Moiré domain tessellations and sliding ferroelectricity, enabling electric-field reconfigurable arrays of localized quantum dots that facilitate tunable long-range quantum state transfer for quantum technologies.

Kunihiro Yananose, Changwon Park, Young-Woo Son2026-05-12🔬 cond-mat.mes-hall